System and method for generating random linked data according to an RDF dataset profile

ABSTRACT

A method, computer program product, and computer system for gathering statistics, by a computing device, for a set of resources associated with a framework. A profile is generated based upon, at least in part, the gathered statistics. A data set is selected for generation of a new resource. The new resource is generated using the profile generated based upon the gathered statistics.

RELATED APPLICATIONS

The subject application is a continuation application of U.S. patentapplication with Ser. No. 14/316,891, filed on Jun. 27, 2014, the entirecontent of which is herein incorporated by reference.

BACKGROUND

The ability to quickly profile and build large data sets both foranalysis and testing (e.g., scale, performance, functional, etc.) may bebeneficial to quickly delivering stable enterprise products. It may benecessary to automate the generation of very large datasets, sincereal-world datasets of that size may not be available. To obtainrealistic measurements from testing, the randomly generated data may berequired to have many of the same characteristics as real-world data.For example, realistic numbers of resources of each type, realisticlinkages between those resources, realistic values for numeric fields,realistic size and content for string fields, etc.

To this end, data generators typically have a great deal of domainspecific knowledge built in. For instance, some data generators may knowabout the different types of resources that may need to be created andthey may know how to generate reasonable values for each of the fieldsthat describe that resource. Generally, this means that different datagenerators may be needed for different applications, and thosegenerators may require updates whenever the “shape” of an application'sdata changes (e.g., new fields are added, new resource types are added,the expected values for a field change, etc).

Other generators may allow more flexibility, but may require users tolearn a new language used for specifying data generation rules. Theshape of the data may be required to be described by a set of datageneration rules and those rules may also require updates whenever theshape of the desired dataset changes. This may also require that theuser creating the rules have an in-depth knowledge of the underlyingresource description framework (RDF) representations created by theapplication the user wishes to test.

BRIEF SUMMARY OF DISCLOSURE

In one implementation, a method, performed by one or more computingdevices, may include but is not limited to gathering statistics, by acomputing device, for a set of resources associated with a framework. Aprofile may be generated based upon, at least in part, the gatheredstatistics. A data set may be selected for generation of a new resource.The new resource may be generated using the profile generated based uponthe gathered statistics.

One or more of the following features may be included. The framework mayinclude a resource description framework. The data set may include atleast one of an existing dataset for augmentation and a new dataset forcreation. Gathering statistics may include running a query on the set ofresources. Gathering statistics may include reading the set of resourcesone quad at a time into memory. The statistics may include uniquerdf:type objects in the set of resources. The statistics may includepredicates that appear in resources for the unique rdf:type objects,wherein the statistics may further include statistics about the uniquerdf:type objects for each predicate.

In another implementation, a computing system includes a processor and amemory configured to perform operations that may include but are notlimited to gathering statistics for a set of resources associated with aframework. A profile may be generated based upon, at least in part, thegathered statistics. A data set may be selected for generation of a newresource. The new resource may be generated using the profile generatedbased upon the gathered statistics.

One or more of the following features may be included. The framework mayinclude a resource description framework. The data set may include atleast one of an existing dataset for augmentation and a new dataset forcreation. Gathering statistics may include running a query on the set ofresources. Gathering statistics may include reading the set of resourcesone quad at a time into memory. The statistics may include uniquerdf:type objects in the set of resources. The statistics may includepredicates that appear in resources for the unique rdf:type objects,wherein the statistics may further include statistics about the uniquerdf:type objects for each predicate.

In another implementation, a computer program product resides on acomputer readable storage medium that has a plurality of instructionsstored on it. When executed by a processor, the instructions cause theprocessor to perform operations that may include but are not limited togathering statistics for a set of resources associated with a framework.A profile may be generated based upon, at least in part, the gatheredstatistics. A data set may be selected for generation of a new resource.The new resource may be generated using the profile generated based uponthe gathered statistics.

One or more of the following features may be included. The framework mayinclude a resource description framework. The data set may include atleast one of an existing dataset for augmentation and a new dataset forcreation. Gathering statistics may include running a query on the set ofresources. Gathering statistics may include reading the set of resourcesone quad at a time into memory. The statistics may include uniquerdf:type objects in the set of resources. The statistics may includepredicates that appear in resources for the unique rdf:type objects,wherein the statistics may further include statistics about the uniquerdf:type objects for each predicate.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features andadvantages will become apparent from the description, the drawings, andthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustrative diagrammatic view of a generator processcoupled to a distributed computing network according to one or moreimplementations of the present disclosure;

FIG. 2 is a diagrammatic view of a client electronic device of FIG. 1according to one or more implementations of the present disclosure;

FIG. 3 is an illustrative flowchart of the generator process of FIG. 1according to one or more implementations of the present disclosure; and

FIG. 4 is an illustrative diagrammatic view of the generator process ofFIG. 1 according to one or more implementations of the presentdisclosure.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

System Overview:

As will be discussed in greater detail below, the present disclosure maydescribe, e.g., a system and method for profiling an existing resourcedescription framework (RDF) dataset and generating new data that may bein line with that profile in a way that may be agnostic to the shape ofthe data. For instance, given a small representative dataset, generatorprocess 10 may enable the generation of a dataset of nearly any sizethat may have many of the same characteristics as the original dataset.Generator process 10 may generate an entirely new dataset, and/or mayaugment an existing dataset, creating links between newly generated andexisting resources.

Generator process 10 may be much more flexible than typical datagenerators that may have domain specific knowledge built in, since,e.g., generator process 10 is able to work with any RDF data. The samedata generator of generator process 10 may be used for any applicationthat may produce RDF data. Additionally, generator process 10 may bemuch easier to use than typical data generators that may be driven by amanually specified set of data generation rules. For example, a userthat may be familiar with the application to be tested may build arepresentative dataset by, e.g., using the application. The user doesnot necessarily require any knowledge of the underlying RDFrepresentations created by the application and the user need not learn anew language for specifying data generation rules.

Moreover, generator process 10 may enable the generation of “very largedatasets” quickly. For instance, as a non-limiting example, startingwith a sample dataset of around 37 MB in size, generator process 10 maygenerate a 2.7 TB dataset (10 billion quads) in about 12 hours.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

Referring now to FIG. 1, there is shown generator process 10 that mayreside on and may be executed by a computer (e.g., computer 12), whichmay be connected to a network (e.g., network 14) (e.g., the internet ora local area network). Examples of computer 12 (and/or one or more ofthe client electronic devices noted below) may include, but are notlimited to, a personal computer(s), a laptop computer(s), mobilecomputing device(s), a server computer, a series of server computers, amainframe computer(s), or a computing cloud(s). Computer 12 may executean operating system, for example, but not limited to, Microsoft®Windows®; Mac® OS X®; Red Hat® Linux®, or a custom operating system.(Microsoft and Windows are registered trademarks of MicrosoftCorporation in the United States, other countries or both; Mac and OS Xare registered trademarks of Apple Inc. in the United States, othercountries or both; Red Hat is a registered trademark of Red HatCorporation in the United States, other countries or both; and Linux isa registered trademark of Linus Torvalds in the United States, othercountries or both).

As will be discussed below in greater detail, generator process 10 maygather statistics, by a computing device, for a set of resourcesassociated with a framework. A profile may be generated based upon, atleast in part, the gathered statistics. A data set may be selected forgeneration of a new resource. The new resource may be generated usingthe profile generated based upon the gathered statistics.

The instruction sets and subroutines of generator process 10, which maybe stored on storage device 16 coupled to computer 12, may be executedby one or more processors (not shown) and one or more memoryarchitectures (not shown) included within computer 12. Storage device 16may include but is not limited to: a hard disk drive; a flash drive, atape drive; an optical drive; a RAID array; a random access memory(RAM); and a read-only memory (ROM).

Network 14 may be connected to one or more secondary networks (e.g.,network 18), examples of which may include but are not limited to: alocal area network; a wide area network; or an intranet, for example.

Computer 12 may include a data store, such as a database (e.g.,relational database, object-oriented database, triplestore database,etc.) and may be located within any suitable memory location, such asstorage device 16 coupled to computer 12. Any data described throughoutthe present disclosure may be stored in the data store. In someimplementations, computer 12 may utilize a database management systemsuch as, but not limited to, “My Structured Query Language” (MySQL®) inorder to provide multi-user access to one or more databases, such as theabove noted relational database. The data store may also be a customdatabase, such as, for example, a flat file database or an XML database.Any other form(s) of a data storage structure and/or organization mayalso be used. Generator process 10 may be a component of the data store,a stand alone application that interfaces with the above noted datastore and/or an applet/application that is accessed via clientapplications 22, 24, 26, 28. The above noted data store may be, in wholeor in part, distributed in a cloud computing topology. In this way,computer 12 and storage device 16 may refer to multiple devices, whichmay also be distributed throughout the network.

Generator process 10 may be accessed via client applications 22, 24, 26,28. Generator process 10 may be a stand alone application, or may be anapplet/application/script/extension that may interact with and/or beexecuted within one or more of client applications 22, 24, 26, 28. Oneor more of client applications 22, 24, 26, 28 may be a stand aloneapplication, or may be an applet/application/script/extension that mayinteract with and/or be executed within and/or be a component ofgenerator process 10. Examples of client applications 22, 24, 26, 28 mayinclude, but are not limited to, e.g., a standard and/or mobile webbrowser, an email client application, a textual and/or a graphical userinterface, a customized web browser, a plugin, an ApplicationProgramming Interface (API), a custom application, or any application tobe tested. The instruction sets and subroutines of client applications22, 24, 26, 28, which may be stored on storage devices 30, 32, 34, 36,coupled to client electronic devices 38, 40, 42, 44, may be executed byone or more processors (not shown) and one or more memory architectures(not shown) incorporated into client electronic devices 38, 40, 42, 44.

Storage devices 30, 32, 34, 36, may include but are not limited to: harddisk drives; flash drives, tape drives; optical drives; RAID arrays;random access memories (RAM); and read-only memories (ROM). Examples ofclient electronic devices 38, 40, 42, 44 (and/or computer 12) mayinclude, but are not limited to, a personal computer (e.g., clientelectronic device 38), a laptop computer (e.g., client electronic device40), a smart/data-enabled, cellular phone (e.g., client electronicdevice 42), a notebook computer (e.g., client electronic device 44), atablet (not shown), a server (not shown), a television (not shown), asmart television (not shown), a media (e.g., video, photo, etc.)capturing device (not shown), and a dedicated network device (notshown). Client electronic devices 38, 40, 42, 44 may each execute anoperating system, examples of which may include but are not limited to,Android™, Apple® iOS®, Mac® OS X®; Red Hat® Linux®, or a customoperating system.

One or more of client applications 22, 24, 26, 28 may be configured toeffectuate some or all of the functionality of generator process 10 (andvice versa). Accordingly, generator process 10 may be a purelyserver-side application, a purely client-side application, or a hybridserver-side/client-side application that is cooperatively executed byone or more of client applications 22, 24, 26, 28 and/or generatorprocess 10.

As one or more of client applications 22, 24, 26, 28, and generatorprocess 10, taken singly or in any combination, may effectuate some orall of the same functionality, any description of effectuating suchfunctionality via one or more of client applications 22, 24, 26, 28and/or generator process 10, and any described interaction(s) betweenone or more of client applications 22, 24, 26, 28 and/or generatorprocess 10 to effectuate such functionality, should be taken as anexample only and not to limit the scope of the disclosure.

Users 46, 48, 50, 52 may access computer 12 and generator process 10(e.g., using one or more of client electronic devices 38, 40, 42, 44)directly through network 14 or through secondary network 18. Further,computer 12 may be connected to network 14 through secondary network 18,as illustrated with phantom link line 54. Generator process 10 mayinclude one or more user interfaces, such as browsers and textual orgraphical user interfaces, through which users 46, 48, 50, 52 may accessgenerator process 10.

The various client electronic devices may be directly or indirectlycoupled to network 14 (or network 18). For example, client electronicdevice 38 is shown directly coupled to network 14 via a hardwirednetwork connection. Further, client electronic device 44 is showndirectly coupled to network 18 via a hardwired network connection.Client electronic device 40 is shown wirelessly coupled to network 14via wireless communication channel 56 established between clientelectronic device 40 and wireless access point (i.e., WAP) 58, which isshown directly coupled to network 14. WAP 58 may be, for example, anIEEE 802.11a, 802.11b, 802.11g, Wi-Fi®, and/or Bluetooth™ device that iscapable of establishing wireless communication channel 56 between clientelectronic device 40 and WAP 58. Client electronic device 42 is shownwirelessly coupled to network 14 via wireless communication channel 60established between client electronic device 42 and cellularnetwork/bridge 62, which is shown directly coupled to network 14.

Some or all of the IEEE 802.11x specifications may use Ethernet protocoland carrier sense multiple access with collision avoidance (i.e.,CSMA/CA) for path sharing.

The various 802.11x specifications may use phase-shift keying (i.e.,PSK) modulation or complementary code keying (i.e., CCK) modulation, forexample. Bluetooth™ is a telecommunications industry specification thatallows, e.g., mobile phones, computers, smart phones, and otherelectronic devices to be interconnected using a short-range wirelessconnection. Other forms of interconnection (e.g., Near FieldCommunication (NFC)) may also be used.

Referring also to FIG. 2, there is shown a diagrammatic view of clientelectronic device 38. While client electronic device 38 is shown in thisfigure, this is for illustrative purposes only and is not intended to bea limitation of this disclosure, as other configurations are possible.For example, any computing device capable of executing, in whole or inpart, generator process 10 may be substituted for client electronicdevice 38 within FIG. 2, examples of which may include but are notlimited to computer 12 and/or client electronic devices 40, 42, 44.

Client electronic device 38 may include a processor and/ormicroprocessor (e.g., microprocessor 200) configured to, e.g., processdata and execute the above-noted code/instruction sets and subroutines.Microprocessor 200 may be coupled via a storage adaptor (not shown) tothe above-noted storage device(s) (e.g., storage device 30). An I/Ocontroller (e.g., I/O controller 202) may be configured to couplemicroprocessor 200 with various devices, such as keyboard 206,pointing/selecting device (e.g., mouse 208), custom device (e.g., device215), USB ports (not shown), and printer ports (not shown). A displayadaptor (e.g., display adaptor 210) may be configured to couple display212 (e.g., CRT or LCD monitor(s)) with microprocessor 200, while networkcontroller/adaptor 214 (e.g., an Ethernet adaptor) may be configured tocouple microprocessor 200 to the above-noted network 14 (e.g., theInternet or a local area network).

The Generator Process:

As discussed above and referring also to FIGS. 3-4, generator process 10may gather 300 statistics, by a computing device, for a set of resourcesassociated with a framework. A profile may be generated 302 by generatorprocess 10 based upon, at least in part, the gathered 300 statistics.Generator process 10 may select 304 a data set for generation of a newresource. The new resource may be generated 306 by generator process 10using the profile generated 302 based upon the gathered 300 statistics.

As noted above, generator process 10 may profile an existing resourcedescription framework (RDF) dataset and generate new data that may be inline with that profile in a way that may be agnostic to the shape of thedata. For instance, given a small representative dataset, generatorprocess 10 may enable the generation of a dataset of nearly any sizethat may have many of the same characteristics as the original dataset.

In some implementations, generator process 10 may gather 300 statistics,by a computing device, for a set of resources associated with aframework. The framework may include a resource description framework.However, it will be appreciated that other frameworks may be usedwithout departing from the scope of the disclosure. As such, the use ofa resource description framework should be taken as an example only andnot to limit the scope of the disclosure.

In some implementations, and referring at least to FIG. 4, generatorprocess 10 may include a resource description framework (RDF) dataprofiler (e.g., profiler 400) and an RDF data generator (e.g., generator402). In some implementations, gathering 300 statistics may includerunning 308 a query on the set of resources. For instance, as will bediscussed in greater detail below, profiler 400 may gather statisticsabout, e.g., an existing RDF dataset by, e.g., running SPARQL queries onthat dataset. The dataset may be stored at, e.g., storage device 16. Aswill also be discussed in greater detail below, data generator 402 mayconsume the information gathered by profiler 400 and may generate newrandom resources that may conform to the statistics gathered by profiler400.

As noted above, in some implementations, gathering 300 statistics mayinclude generator process 10 running 308 a query on the set ofresources. In some implementations, the statistics may include uniquerdf:type objects in the set of resources. In some implementations, thestatistics may include predicates that appear in resources for theunique rdf:type objects, wherein the statistics may further includestatistics about the unique rdf:type objects for each predicate. Forinstance, profiler 400 may run, e.g., SPARQL Protocol and RDF QueryLanguage (SPARQL) (or other relevant query language), one or morequeries to gather 300 statistics about some or all of the resources inthe sample dataset (e.g., the above-noted set of resources) provided.Profiler 400 may begin by gathering 300 a list or other data constructof some or all the unique rdf:type objects in the sample dataset. Insome implementations, for each type that is found, profiler 400 maygather 300 a list of some or all predicates that may appear in resourcesof that type. In some implementations, for each predicate that is found,profiler 400 may gather 300 statistics about the objects of thatpredicate for resources of that type.

In some implementations, a profile may be generated 302 by generatorprocess 10 based upon, at least in part, the gathered 300 statistics.For example, the (final) profile that may be generated 302 may includethe following example and non-limiting information: (1) Distribution ofresources by type (e.g., total number of resources of each type, and thepercentage); (2) For each predicate, the minimum, maximum, and averagenumber of times that predicate occurs in resources of this type; (3) Foreach predicate, the type of value expected in the object; (4) Forpredicates where varying string values are observed in the object bygenerator process 10, the minimum, maximum, average, and standarddeviation of the string length; (5) For predicates where a fixed set ofstring values are repeatedly observed in the object by generator process10, the set of values observed and the number of times each valueoccurred; (6) For predicates with a numeric value expected in theobject, the minimum, maximum, average, and standard deviation of thevalue; (7) For predicates with a date value expected in the object, theminimum, maximum, average, and standard deviation of the value(converted to unix time); (8) For predicates with a Boolean valueexpected in the object, the percentage of the observed values which were‘true’; (9) For predicates with a URI value expected in the object wherethe URI points to another resource in the dataset, the rdf:type of thatresource; (10) For predicates with a URI value expected in the objectwhere the URI is not found in the dataset, the set of values observed bygenerator process 10 and the number of times each value occurred.

In some implementations, gathering 300 statistics may include generatorprocess 10 reading 310 the set of resources one quad at a time intomemory. For example, generator process 10 may reading 310 the dataset(e.g., the above-noted set of resources) one quad at a time into memory(e.g., an in-memory Java-based data structure), which may provide moreflexibility and may provide better performance than using SPARQL (orother relevant query language). In some implementations, profiler 400may read 310 data in nquads format and build up a data structure thatgroups quads by the graph URI. Some or all of the quads that may make upa graph may define a single resource. After generator process 10 groupsthe data by each graph URI, profiler 400 may iterate over each of thegraphs, gathering 300 the relevant statistics about each one. In someimplementations, profiler 400 may first determines the rdf:type of thegraph by looking for quads in the graph with the graph URI as thesubject and rdf:type as the predicate. The statistics that are gathered300 may be first grouped by the rdf:type of the graph. In someimplementations, most quads in a graph may be making statements aboutthat same resource (i.e., the graph URI and the subject URI may be thesame), but that is not always true. Statistics may next be grouped bythe subject of the quads in each graph as follows in the example: (A)One group for all of the predicates and objects observed by generatorprocess 10 where the graph URI and subject URI are the same; (B) If thesubject is a URI that points to another graph in the dataset, one groupfor the rdf:type of each subject that is observed by generator process10; (C) If the subject is a URI that is not a graph in the dataset, onegroup for the rdf:type of each subject that is observed by generatorprocess 10 (D) If the subject is a blank node (e.g., an id that may bereferenced by other quads in the same graph, or is the subject ofcertain triples in a graph and the object of others), one group for therdf:type of each subject that is observed by generator process 10.

For each subject grouping, profiler 400 may gather 300 statistics abouteach predicate that is observed (e.g., number of occurrences within thatsubject and statistics about the object values for that predicate). Theobject statistics that are gathered 300 may be very similar to theabove-noted object statistics with one or more additions. For example,profiler 400 may recognize different object types, gather 300 statisticsabout each separately and gather 300 information about the distribution.For example, a certain predicate may contain a string value in somecases and a numeric value in other cases. Profiler 400 may recognizethat, e.g., ⅔ of the time, the predicate has a string value with certainproperties and, e.g., ⅓ of the time the predicate has a numeric valuewith other properties.

In some implementations, generator process 10 may recognize objectvalues that point to blank nodes or other subject URIs that may becontained in the same graph. For instance, profiler 400 may assign eachsubject grouping a unique identifier that is being used in the objectstatistics to identify cases where the object of a quad may be areference to the subject of one or more other quads in this graph.

In some implementations, generator process 10 may select 304 a data setfor generation of a new resource. In some implementations, the data setmay include at least one of an existing dataset for augmentation and anew dataset for creation. In some implementations, if a user (e.g., user46) specifies, e.g., via a user interface of generator process 10, adataset to be augmented, data generator 402 may include resources fromthat dataset when selecting (e.g., randomly) a resource of a certaintype to which to link. Thus, multiple runs of generator 402 may producea single fully linked dataset.

In some implementations, the new resource may be generated 306 bygenerator process 10 using the profile generated 302 based upon thegathered 300 statistics. For example, generator 402 may consume theabove-noted statistics gathered 300 by profiler 400, generate 306 newrandom resources based on those statistics and produce a stream ofnquads formatted output. Generator 402 may use inputs for the number ofresources that may be generated 306 and, e.g., a sample text file thatmay be used for producing string values. Generator 402 may optionallyaccept a dataset to be augmented and a minimum date for generating datevalues.

For each resource that is to be generated 306, generator 402 mayrandomly select the type of resource to be generated 306 based on thetype distribution from the above-noted profile. Given the type ofresource to be generated 306, generator 402 may decide to add a triplewith each predicate in the profile for that resource type zero or moretimes based on, at least in part, the above-noted minimum, maximum, andaverage number of occurrences of that predicate in resources of thattype. When generating 306 the object of a triple, generator 402 may usestatistics about that object from the above-noted profile as follows:(1) For objects with varying string values in the profile, generator 402may randomly decide on a length of the string based on the minimum,maximum, average, and standard deviation of the string length from theprofile. Generator 402 may add words from the provided sample text fileto the string value until the length of the string is greater than orequal to the randomly determined string length; (2) For objects with afixed set of string values, one of the values from the profile may beselected at random (or otherwise) based on the distribution of thosevalues in the profile; (3) For objects with a numeric value, a value maybe randomly generated 306 using the minimum, maximum, average, andstandard deviation from the profile; (4) For objects with a date value,a value may be randomly generated 306 using the minimum, maximum,average, and standard deviation from the profile if no minimum date wasspecified as input. Otherwise, a random value between the minimum dateand the current date may be generated 306; (5) For objects with aBoolean value, a value may be randomly chosen by generator process 10with the same true/false probability as in the profile; (6) For objectswith a URI value where the profile specifies an rdf:type for the object,generator 402 may randomly select an object of that type to which tolink. If no object of that type exists, one may be generated bygenerator process 10; (7) For objects with a URI value where the profilespecifies a fixed set of possible values, a value may be selected atrandom by generator process 10 based on, at least in part, thedistribution of the values in the profile.

In some implementations, such as where generator process 10 reads 310the set of resources one quad at a time into memory, generator 402 mayfirst pick a graph type to create based on the distribution in theabove-noted profile. For that graph, generator 402 may determine whichsubjects to generate 306 based on, at least in part, the subjects thatwere observed in the profile by generator process 10. For each subject,the corresponding predicates and objects may be generated 306 accordingto the profile.

The terminology used herein is for the purpose of describing particularimplementations only and is not intended to be limiting of thedisclosure. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps (notnecessarily in a particular order), operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps (not necessarily in a particular order),operations, elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications,variations, and any combinations thereof will be apparent to those ofordinary skill in the art without departing from the scope and spirit ofthe disclosure. The implementation(s) were chosen and described in orderto best explain the principles of the disclosure and the practicalapplication, and to enable others of ordinary skill in the art tounderstand the disclosure for various implementation(s) with variousmodifications and/or any combinations of implementation(s) as are suitedto the particular use contemplated.

Having thus described the disclosure of the present application indetail and by reference to implementation(s) thereof, it will beapparent that modifications, variations, and any combinations ofimplementation(s) (including any modifications, variations, andcombinations thereof) are possible without departing from the scope ofthe disclosure defined in the appended claims.

What is claimed is:
 1. A computer-implemented method comprising:gathering statistics, by a computing device, for a set of resourcesassociated with a framework, wherein gathering statistics includesreading the set of resources in an N-Quads format, wherein the N-Quadsformat includes a graph URI, wherein the statistics include uniquerdf:type objects in the set of resources and further include predicatesthat appear in resources for the unique rdf:type objects; generating aprofile based upon, at least in part, the gathered statistics, whereinthe profile generated includes, at least in part, a minimum, maximum,average, and standard deviation of at least one of a string value and anumerical value for each predicate; selecting a data set for generationof a new resource associated with the framework with one or more of theplurality of characteristics of the set of resources based upon, atleast in part, the graph URI, wherein the data set includes an existingdataset for augmentation; generating the new resource associated withthe framework using the profile generated based upon the gatheredstatistics, including selecting a graph type to create based on adistribution included within the profile, wherein generating the newresource associated with the framework includes generating new, randomdata with the one or more of the plurality of characteristics of the setof resources using the profile, wherein, upon selecting the existingdataset for augmentation, generating the new resource associated withthe framework includes randomly selecting a resource from the existingdataset for augmentation to which to link, and producing a single fullylinked dataset; and testing an application with the new resourceassociated with the framework.
 2. The computer-implemented method ofclaim 1 wherein the framework includes a resource description framework.3. The computer-implemented method of claim 1 wherein gatheringstatistics includes running a query on the set of resources.
 4. Thecomputer-implemented method of claim 1 wherein gathering statisticsincludes reading the set of resources one quad at a time into memory. 5.The computer-implemented method of claim 1 wherein the statisticsinclude statistics about the unique rdf:type objects for each predicate.